Highly sensitive detection platform-based diagnosis of oesophageal squamous cell carcinoma in China: a multicentre, case–control, diagnostic study

Abstract

Early detection and screening of oesophageal squamous cell carcinoma rely on upper gastrointestinal endoscopy, which is not feasible for population-wide implementation. Tumour marker-based blood tests offer a potential alternative. However, the sensitivity of current clinical protein detection technologies is inadequate for identifying low-abundance circulating tumour biomarkers, leading to poor discrimination between individuals with and without cancer. We aimed to develop a highly sensitive blood test tool to improve detection of oesophageal squamous cell carcinoma. We designed a detection platform named SENSORS and validated its effectiveness by comparing its performance in detecting the selected serological biomarkers MMP13 and SCC against ELISA and electrochemiluminescence immunoassay (ECLIA). We then developed a SENSORS-based oesophageal squamous cell carcinoma adjunct diagnostic system (with potential applications in screening and triage under clinical supervision) to classify individuals with oesophageal squamous cell carcinoma and healthy controls in a retrospective study including participants (cohort I) from Sun Yat-sen University Cancer Center (SYSUCC; Guangzhou, China), Henan Cancer Hospital (HNCH; Zhengzhou, China), and Cancer Hospital of Shantou University Medical College (CHSUMC; Shantou, China). The inclusion criteria were age 18 years or older, pathologically confirmed primary oesophageal squamous cell carcinoma, and no cancer treatments before serum sample collection. Participants without oesophageal-related diseases were recruited from the health examination department as the control group. The SENSORS-based diagnostic system is based on a multivariable logistic regression model that uses the detection values of SENSORS as the input and outputs a risk score for the predicted likelihood of oesophageal squamous cell carcinoma. We further evaluated the clinical utility of the system in an independent prospective multicentre study with different participants selected from the same three institutions. Patients with newly diagnosed oesophageal-related diseases without previous cancer treatment were enrolled. The inclusion criteria for healthy controls were no obvious abnormalities in routine blood and tumour marker tests, no oesophageal-associated diseases, and no history of cancer. Finally, we assessed whether classification could be improved by integrating machine-learning algorithms with the system, which combined baseline clinical characteristics, epidemiological risk factors, and serological tumour marker concentrations. Retrospective SYSUCC cohort I (randomly assigned [7:3] to a training set and an internal validation set) and three prospective validation sets (SYSUCC cohort II [internal validation], HNCH cohort II [external validation], and CHSUMC cohort II [external validation]) were used in this step. Six machine-learning algorithms were compared (the least absolute shrinkage and selector operator regression, ridge regression, random forest, logistic regression, support vector machine, and neural network), and the best-performing algorithm was chosen as the final prediction model. Performance of SENSORS and the SENSORS-based diagnostic system was primarily assessed using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Between Oct 1, 2017, and April 30, 2020, 1051 participants were included in the retrospective study. In the prospective diagnostic study, 924 participants were included from April 2, 2022, to Feb 2, 2023. Compared with ELISA (108·90 pg/mL) and ECLIA (41·79 pg/mL), SENSORS (243·03 fg/mL) showed 448 times and 172 times improvements, respectively. In the three retrospective validation sets, the SENSORS-based diagnostic system achieved AUCs of 0·95 (95% CI 0·90-0·99) in the SYSUCC internal validation set, 0·93 (0·89-0·97) in the HNCH external validation set, and 0·98 (0·97-1·00) in the CHSUMC external validation set, sensitivities of 87·1% (79·3-92·3), 98·6% (94·4-99·8), and 93·5% (88·1-96·7), and specificities of 88·9% (75·2-95·8), 74·6% (61·3-84·6), and 92·1% (81·7-97·0), respectively, successfully distinguishing between patients with oesophageal squamous cell carcinoma and healthy controls. Additionally, in three prospective validation cohorts, it yielded sensitivities of 90·9% (95% CI 86·1-94·2) for SYSUCC, 84·8% (76·1-90·8) for HNCH, and 95·2% (85·6-98·7) for CHSUMC. Of the six machine-learning algorithms compared, the random forest model showed the best performance. A feature selection step identified five features to have the highest performance to predictions (SCC, age, MMP13, CEA, and NSE) and a simplified random forest model using these five features further improved classification, achieving sensitivities of 98·2% (95% CI 93·2-99·7) in the internal validation set from retrospective SYSUCC cohort I, 94·1% (89·9-96·7) in SYSUCC prospective cohort II, 88·6% (80·5-93·7) in HNCH prospective cohort II, and 98·4% (90·2-99·9) in CHSUMC prospective cohort II. The SENSORS system facilitates highly sensitive detection of oesophageal squamous cell carcinoma tumour biomarkers, overcoming the limitations of detecting low-abundance circulating proteins, and could substantially improve oesophageal squamous cell carcinoma diagnostics. This method could act as a minimally invasive screening tool, potentially reducing the need for unnecessary endoscopies. The National Key R&D Program of China, the National Natural Science Foundation of China, and the Enterprises Joint Fund-Key Program of Guangdong Province. For the Chinese translation of the abstract see Supplementary Materials section.